Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Novel Mutations In Genes Causing Hereditary Spastic Paraplegia And Charcot-Marie-Tooth Neuropathy Identified By An Optimized Protocol For Homozygosity Mapping Based On Whole-Exome Sequencing

Purpose: Homozygosity mapping is an effective approach for detecting molecular defects in consanguineous families by delineating stretches of genomic DNA that are identical by descent. Constant developments in next-generation sequencing created possibilities to combine whole-exome sequencing (WES) and homozygosity Mapping in a single step. Methods: Basic optimization of homozygosity mapping parameters was performed in a group of families with autosomal-recessive (AR) mutations for which both single-nucleotide polymorphism (SNP) array and WES data were available. We varied the criteria for SNP extraction and PLINK thresholds to estimate their effect on the accuracy of homozygosity mapping based on WES. Results: Our protocol showed high specificity and sensitivity for homozygosity detection and facilitated the identification of novel mutations in GAN, GBA2, and ZFYVE26 in four families affected by hereditary spastic paraplegia or Charcot-Marie-Tooth disease. Filtering and mapping with optimized parameters was integrated into the HOMWES (homozygosity mapping based on WES analysis) tool in the GenomeComb package for genomic data analysis. Conclusion: We present recommendations for detection of homozygous regions based on WES data and a bioinformatics tool for their identification, which can be widely applied for studying AR disorders.WoSScopu

Crowdsourcing digital health measures to predict Parkinson’s disease severity: The Parkinson’s Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)